Blueshift provides many pre-configured recommendation recipes that are created for industry specific use cases and which take into account individual user behavior and aggregate behavior over all users.
When you select a recipe from the Content tab, you can view all the available recipes along with the data requirements, inputs and use cases. Each recipe provides controls for contextual input specific to that recipe, past messaging and behavior suppression, filters for audience specific logic and sorting options.
Recipes are broadly mapped to following user journey touch points throughout their life cycle. Consider picking a recipe based on the intended audience.
User Life Cycle
Popular or trending items site wide or specific catalog attributes
Similar Items to recent activity
Similar Items other users like them consider
Next Best Items based on recent activity
Related Items based on frequent visits
Related Items to current session viewing history
Affinity Items ordered by popularity or auto optimized
Items based on explicit user preferences/subscriptions
Alerts like price drops, low in stock or new in collection
Predictive Content feeds auto optimized based on engagement
Intermittently Active Users
Affinity Items based ordered by recency or auto optimized
Alerts like new in collections, seasonal discounts, back in stock
Predictive Content feeds auto optimized based on prior activity
Winback promotions ordered by popularity
Predictive Content based on previous transactions
Promotional Content related to prior activity
Affinity items ordered by recency or auto optimized
Predictive content based on user attributes
Seasonal Items ordered by popularity or auto optimized